Prediction Of Product Yield Research Articles

Application of Artificial Intelligent Tools to Modeling of Glucosamine Preparation from Exoskeleton of Shrimp

The objective of this study was to forecast and optimize the glucosamine production yield from chitin (obtained from Persian Gulf shrimp) by means of genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural networks (ANNs) as tools of artificial intelligence methods. Three factors (acid concentration, acid solution to chitin ratio, and reaction time) were used as the input parameters of the models investigated. According to the obtained results, the production yield of glucosamine hydrochloride depends linearly on acid concentration, acid solution to solid ratio, and time and also the cross-product of acid concentration and time and the cross-product of solids to acid solution ratio and time. The production yield significantly increased with an increase of acid concentration, acid solution ratio, and reaction time. The production yield is inversely related to the cross-product of acid concentration and time. It means that at high acid concentrations, the longer reaction times give lower production yields. The results revealed that the average percent error (PE) for prediction of production yield by GA, PSO, and ANN are 6.84, 7.11, and 5.49%, respectively. Considering the low PE, it might be concluded that these models have a good predictive power in the studied range of variables and they have the ability of generalization to unknown cases.

215 原子比による草本バイオマスの熱分解・ガス化収率予測(GS-1 逆問題,乱流)

215 原子比による草本バイオマスの熱分解・ガス化収率予測(GS-1 逆問題,乱流)

Thermal Gasification of Polypropylene -Prediction of Product Yield Based on Intensity Function of Pyrolysis Conditions-

Studies on gasification of polypropylene under atmospheric pressure have been carried out by using an apparatus consisting of a flow system of fixed bed reactor, into which the overheate d steam was conducted as a heat carrier.In this study, by making an experimental equation of intensity function (IF) concerning pyrolysis conditions, the product yield was estimated.IF is dependent only upon reaction temper ature and residence time and is expressed by the following equation: IF=Tθ0.05 with reference to methane, where θ is the residence time (sec), T is temperature (°F) and methane is assumed to be a final product of pyrolysis.As IF correlates with product yields (as shown in Figs.4, 5, 6 and 7 ), the product yield could be successfully predicted by appropriate choice of pyrolysis conditions.